The invention relates to searching product catalogs and relates particularly, though not exclusively, to improved methods suitable for allowing shoppers to effectively search online product catalogs of items able to purchased from a retailer.
With the growing popularity of e-commerce, searching online catalogs for products has become an important problem for both online retailing for business to consumer (B2C) as well as business to business (B2B) trading portals. These Web sites are virtual shops that have searchable catalogs providing product details.
A shopper may either browse through the various products for sale, or select products by specific product identification numbers. When using conventional product catalogs, the shopper may either (a) know the appropriate product identification number, for example, when ordering a replacement item; (b) be looking for items similar to some item in mind, for example, when looking for a shirt; or (c) be browsing with no specific product in mind but hoping to make up her mind as she looks through the range of products available for sale, for example, when shopping for a gift.
Prior-art commerce systems like WebSphere(trademark) Commerce Suite from IBM, Open Market(trademark) from Open Market Inc, and Broad Vision(trademark) from Broad Vision Inc save product information as numeric and nominal values in a database. These product attributes form a high-dimensional space where each product occupies a unique point. Exact, partial and range queries may be performed using vectors from this space to retrieve products, and present them to the shopper.
Typically, the shopper wants to search for products by description, that is, product attributes rather than by product identification numbers since these may differ from store to store. Further, she may want to browse through the catalog and explore the various possibilities to decide what suits her requirements. It is often not practical or convenient to do exact searches on catalog databases due to several reasons. As a consequence, similarity searching has been proposed to enable xe2x80x9cvaguexe2x80x9d or xe2x80x9cfuzzyxe2x80x9d querying of the product catalog. However, one limitation of such systems is that different shoppers have different notions of similarity from one another. Hence, two products may be similar to each other in one shopper""s perception but another shopper may find them quite different. The use of fixed similarity metrics to evaluate closeness of two products has limitations since it cannot capture the subjectivity of shoppers"" product requirements. For example, a shopper may find a Toyota Camry motor vehicle similar to a Volkswagen Wagon since their engines have similar horsepower, but another shopper find a Honda Accord more similar to the Camry since they are both sedans.
Another key limitation of existing commerce systems is their inability to capture the rich content available in pictures of the actual product. Typically, details about visual properties of products such as appearance, colour distribution, texture and so on, are represented by keywords and stored in a multi-attribute product catalog. However, there are cases in which keywords cannot adequately describe product characteristics such as floral prints, upholstery patterns, jewellery designs and so on. Further, describing visual characteristics is often a subjective process and hence it is not possible to appropriately associate unique keywords from pictures of products to enable a search based on a product""s visual appearance. Thus, if a shopper wants to search upholstery that looks similar to a pattern that a shopper has with her, then an annotation-based catalog does not work well, as the shopper may not be able to describe the pattern in words. She may have to browse through the catalog for her requirements even though she has a pattern in her mind.
U.S. Pat. No. 4,996,642 (Hey), titled xe2x80x9cSystem and method for recommending itemsxe2x80x9d, discloses a system in which a user is recommended a product, such as a movie title in a video store, based on the similarity of the current user and other users. This system is built on the hypothesis that if two users have generally bought similar things in the past, then they will tend to buy similar products in future.
However, any given shopper typically has different product requirements at different times and hence, such recommendations cannot effectively be made depending solely on her and/or other shoppers"" previous responses. Such systems are based on the shoppers"" histories and profiles, and are thus xe2x80x9cstate-lessxe2x80x9d, that is, the shopper""s interaction with the system during a shopping session is not used to recommend items or find items closer to her requirements.
U.S. Pat. No. 6,041,311 (Chislenko et al), titled xe2x80x9cMethod and apparatus for item recommendation using automated collaborative filteringxe2x80x9d, discloses a collaborative filtering approach to product recommendation. It is claimed that content-based systems do not work well and hence products must be matched only at the higher level. Though feature selection and extraction remains a difficult problem, partial solutions do exist for specific domains. In particular, extensive research in image and video analysis has resulted in acceptable automatic feature extraction methods. These methods work even better when assisted by an expert.
U.S. Pat. No. 5,666,442 (Wheeler), titled xe2x80x9cComparison system for identifying the degree of similarity between objects by rendering a numeric measure of closeness, the system including all available information complete with errors and inaccuraciesxe2x80x9d, discloses a method of similarity searching where attributes can be weighted by the user and where the all items may not have all the feature attributes. This work has been incorporated in the xe2x80x9cSimilarity Search Enginexe2x80x9d from Infoglide Inc, a search engine that returns a rank-order list of items weighted according to the preferences given by the user at query time. The results are ranked with the items most closely matching the search criteria at the top of the list. The system allows the results of multiple databases searches to be brought together. In such a system, the user has to explicitly give the weights for each search criteria. This can make the system difficult to use, especially if the features used for searching can not be adequately understood by the user. Further, this system is better suited to cases in which a definite object is being searched (for example, a criminal record from a police database). However, while shopping, a shopper may start with only an approximate query and arrive at decision only after exploring the product offerings from the store, such as when selecting a gift.
Ivo Vollrath, Wolfgang Wilke, and Ralph Bergmann present a case-based reasoning approach to product selection using intelligent online catalogs in Intelligent Electronic Catalogs for Sales Support: Introducing Case-Based Reasoning Techniques to On-Line Product Selection Applications, R. Roy, T. Furuhashi, P. K. Chawddhry, (Eds.): Advances in Soft Computingxe2x80x94Engineering Design and Manufacturing, Springer-Verlag, London, 1999. The described system, however, expects the user to weigh the relative importance of different attributes during the search process.
None of the above product search approaches outlined above are rigorously optimal or universally applicable for use in presenting product information to shoppers. Accordingly, it is an object of the invention to at least attempt to address these and other limitations associated with the prior art. In particular, it is an object of the invention to generally improve the results provided to those searching online databases, such as retail product catalogs.
The inventive concept resides in a recognition that the searching of online catalog databases by shoppers is advantageously improved by performing similarity searching on searches performed by the shopper, in conjunction with adjusting the similarity metric used during the search to interactively improve the relevance of the resulting search results to the shopper. Preferably, this involves using relevance feedback and/or product redefinition in an attempt to learn the xe2x80x9cimplied conceptxe2x80x9d of the shopper""s stated product requirements. That is, the inventive concept involves an attempt to understand the concept implied by the shopper""s stated requirements to enhance the quality of the search results to the shopper, rather than attempting to enhance search results using, for example, the shopper""s past orders or previous actions, or the past orders or previous actions of other shoppers.
It is recognised that shoppers can often be thought of having a latent implied concept that is not particularly suitable or able to be readily expressed or presented to a search interface of a product catalog. In contrast with the prior art, a state of the shopper""s actions is maintained during a search or query session, but not used across different shopping sessions of the same or different shoppers.
More particularly, the inventive concept involves using similarity searches for querying product catalogs, and relevance feedback techniques and product specification modification to learn the shopper""s requirements iteratively and interactivity rather than expecting her, for example, to explicitly identify and weight product various attributes which may or may not be of particular interest. The inventive concept also uses techniques from content-based image retrieval systems to enable product searches based on their visual properties. In particular, product specification modification provides a method of personalising products whereby the shopper may modify the approximate product attributes and visually inspect the changes before placing an order for the modified product, or using the modified product as a basis for further catalog searching.
Preferred embodiments of the invention address conventional limitations of the prior art by providing for personalised product catalog searches in a multi-attribute space using a similarity metrics and relevance feedback. Preferred embodiment also allows a shopper to search using the product images. The inventive embodiments also infer the shopper""s requirement and allow a shopper to visually verify the effects of modifying existing products.
Accordingly, in one aspect, the invention provides a method of providing to a user an interactive product search facility suitable for searching product information stored in a product catalog, the method comprising the steps of:
(a) providing a database of records respectively describing products, each of said records including a plurality of fields containing respective field values which characterise said products;
(b) receiving from a user a request to search said database of records in the form of a query based on one or more of said fields;
(c) providing to the user an initial set of similar search records found as a result of a similarity search based on said search request;
(d) receiving from the user relevance feedback as to which of said similar search records are of greater and/or lesser relevance to the user compared with other similar search records;
(e) providing to the user a modified set of similar search results found as a result of a similarity search based on (i) said search request, and (ii) said relevance feedback;
wherein said modified set of similar search results provides to the user records which are generally of greater relevance to said user than said initial set of similar search results.
The search requests involve numeric and nominal product attributes, as well as product image attributes, where appropriate.
Similarity searches are performed on the basis of similarity functions, and different similarity functions are used for nominal and non-nominal attributes. The similarity function for numeric product attributes is Mahalanobis distance, more particularly a weighted Euclidean distance measure. Relative weights are assigned to respective attributes to represent the relative importance of that attribute in the similarity function.
In preferred embodiments, providing sets of similar search records involves multimodal similarity matching of numeric, nominal and image product attributes.
The relevance feedback received from the user is used to automatically adjust the relative weights assigned to respective product attributes to retrieve search results that are closer to the concept implied by the user""s search query and relevance feedback. Preferably, steps (c) and (d) may be performed a plurality of times.
The initial set of similar search results includes K-nearest neighbours using the designated similarity function. The relevance feedback involves an indication from the user, for one or more of the results in the set of similar search results, whether the result is relevant, irrelevant, or indifferent. On the basis of the relevance feedback, the similarity function, or parameters thereof, are modified so that the results of the modified similarity search provide the modified set of similar search results which better matches those of the initial set of similar search results designated relevant or irrelevant.
In an alternative embodiment, the initial set of similar search results is not the K-nearest neighbours, but are quite different from each other and the query point, to assist in catalog exploration, and to better orient the similarity function to the shopper""s implied concept.
Before providing an initial set of similar search results, a number of similar product specifications are generated automatically, by modifying for each product one product attribute. Shopper feedback is received for these modified products, in order to adjust the query point and learn the similarity metric that is most suitable, prior to conducting a similarity search.